基于椭圆曲线密码体制的盲身份认证研究

提出了一个基于椭圆曲线密码体制的盲数字签名方案,并在此基础上设计了一个盲身份认证方案,该方案的安全性是建立在目前还没有有效攻击方法的有限域上非超奇异椭圆曲线的离散对数问题之上的,它能够防止非法用户对系统的访问以及非法用户对合法用户信息的窃取,由于在认证过程中涉及到有限域上椭圆曲线的模逆和点和,运算相对简单,因而实现也较为容易,从理论上讲方案是安全的,并且具有较低的复杂性,对于电子商务和电子投票应用场合具有一定的实用价值。     

Exact solution of a class of nonlinear knapsack problems

We consider a class of nonlinear knapsack problems with applications in service systems design and facility location problems with congestion. We provide two linearizations and their respective solution approaches. The first is solved directly using a commercial solver. The second is a piecewise linearization that is solved by a cutting plane method.     

求解0-1背包问题的两种方法的分析与比较

背包问题(KP)是计算机科学中典型的NP-hard问题,不存在多项式时间的精确算法。本文首先给出了求解0-1KP问题的一种改进的近似算法,讨论了算法复杂度与近似比;然后,给出了求解0-1KP的动态规划算法描述,并分析了算法的复杂度;最后,对两种方法进行了理论分析,并利用3个较大规模0-1KP实例的仿真计算结果与GDPSO进行比较。     

多元一次不定方程解的结构及其应用

初等数论是密码学研究的重要基础理论.引入多元一次不定方程的概念,利用多元一次不定方程解的存在性条件和二元一次不定方程一般解的结构,采用递推的数学归纳法,得到并证明了多元一次不定方程一般解及其特解的结构形式.进一步研究并给出了多元一次同余方程非负整数解的存在性条件,在此基础之上利用这个存在性条件对RSA公钥密码体制进行了密钥多元化的改进,论证了其加解密算法的正确性.最后通过例解说明改进后的RSA公钥密码体制较原密码体制更为安全可靠且易于实现.     

云计算在RSA密码体制分析中的应用

RSA是著名的公钥密码体制之一,其安全性是基于大整数因子分解困难性假设的,求解RSA问题的最直接方法是分解RSA的模数.由于云计算的大规模分布式计算能力,一些使用分布式计算模型MapReduce的大整数分解手段已经实现,针对大整数因子分解的问题,提出了给定范围内搜索因子的新方法,并对相应的实验数据和结果进行了分析.结果表明,在云中的分布式计算的运行时间远小于单台机器.     

基于乘法群Z_p~*的ELGamal公钥密码系统

为了解决发送方与接收方不能识别对方身份的问题,在研究ElGamal密码体制的基础上,给出了一种基于乘法群Zp*的ELGamal公钥密码体制。结果表明:该算法可以实现通信双方的双向身份认证,有效地防止了攻击者冒充发送方伪造要发送的消息。同时该算法在通信过程中加入了可以追踪消息来源的信息,使得接收者可以对消息的真实性进行有效的验证。通过对消息的双重保护,该体制实现了在公开信道上的安全通信。     

Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems

This study analyzes multiobjective d-dimensional knapsack problems (MOd-KP) within a comparative analysis of three multiobjective evolutionary algorithms (MOEAs): the ε-nondominated sorted genetic algorithm II (ε-NSGAII), the strength Pareto evolutionary algorithm 2 (SPEA2) and the ε-nondominated hierarchical Bayesian optimization algorithm (ε-hBOA). This study contributes new insights into the challenges posed by correlated instances of the MOd-KP that better capture the decision interdependencies often present in real world applications. A statistical performance analysis of the algorithms uses the unary ε-indicator, the hypervolume indicator and success rate plots to demonstrate their relative effectiveness, efficiency, and reliability for the MOd-KP instances analyzed. Our results indicate that the ε-hBOA achieves superior performance relative to ε-NSGAII and SPEA2 with increasing number of objectives, number of decisions, and correlative linkages between the two. Performance of the ε-hBOA suggests that probabilistic model building evolutionary algorithms have significant promise for expanding the size and scope of challenging multiobjective problems that can be explored.     

A novel cryptosystem based on grey system theory and genetic algorithm

In this paper, we first attempt to introduce the biological concepts of genetic factor and genetic variation into the study of a Kaleidoscope–Queer (in brief, K–Q) cryptosystem. The novel K–Q cryptosystem including the genetic factor, (GM, X, λ(x)), can be applied to ensure the security of an information system. The main contributions include the K–Q cryptosystem concept with genetic factor, the existence theorem of -era K–Q cryptosystem with genetic factor, the limited embedded theorem of K–Q cryptosystem with genetic factor, and the genetic variability theorem of K–Q cryptosystem.     

Competitive facility location model with concave demand

We consider a spatial interaction model for locating a set of new facilities that compete for customer demand with each other, as well as with some pre-existing facilities to capture the “market expansion” and the “market cannibalization” effects. Customer demand is assumed to be a concave non-decreasing function of the total utility derived by each customer from the service offered by the facilities. The problem is formulated as a non-linear Knapsack problem, for which we develop a novel solution approach based on constructing an efficient piecewise linear approximation scheme for the objective function. This allows us to develop exact and α-optimal solution approaches capable of dealing with relatively large-scale instances of the model. We also develop a fast Heuristic Algorithm for which a tight worst-case error bound is established.